from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 1s
KMeans_short: 0h 0m 2s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 8s
Ridge: 0h 0m 11s
LogisticRegression: 0h 0m 19s
KMeans_tall: 0h 0m 21s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 28s
daal4py_KNeighborsClassifier: 0h 2m 25s
KNeighborsClassifier_kd_tree: 0h 2m 35s
xgboost: 0h 5m 1s
catboost_symmetric: 0h 5m 2s
catboost_lossguide: 0h 5m 4s
lightgbm: 0h 5m 7s
HistGradientBoostingClassifier: 0h 5m 8s
KNeighborsClassifier: 0h 33m 52s
total: 1h 5m 57s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.197 | 0.000 | 4.064 | 0.000 | 1 | 1 | NaN | NaN | 0.489 | 0.000 | 0.403 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 15.530 | 0.266 | 0.000 | 0.016 | 1 | 1 | 0.726 | 0.808 | 1.722 | 0.007 | 9.018 | 0.159 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.222 | 0.001 | 0.000 | 0.222 | 1 | 1 | 1.000 | 1.000 | 0.088 | 0.001 | 2.530 | 0.023 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.157 | 0.000 | 5.100 | 0.000 | -1 | 100 | NaN | NaN | 0.476 | 0.000 | 0.329 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 37.770 | 0.000 | 0.000 | 0.038 | -1 | 100 | 0.933 | 0.934 | 1.769 | 0.004 | 21.348 | 0.053 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.216 | 0.015 | 0.000 | 0.216 | -1 | 100 | 1.000 | 1.000 | 0.087 | 0.000 | 2.481 | 0.169 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.158 | 0.000 | 5.058 | 0.000 | 1 | 5 | NaN | NaN | 0.475 | 0.000 | 0.333 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.323 | 1.344 | 0.000 | 0.022 | 1 | 5 | 0.832 | 0.717 | 1.720 | 0.005 | 12.981 | 0.783 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.210 | 0.001 | 0.000 | 0.210 | 1 | 5 | 1.000 | 1.000 | 0.087 | 0.000 | 2.414 | 0.009 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.132 | 0.000 | 6.080 | 0.000 | 1 | 100 | NaN | NaN | 0.477 | 0.000 | 0.276 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 20.841 | 0.047 | 0.000 | 0.021 | 1 | 100 | 0.933 | 0.717 | 1.720 | 0.008 | 12.119 | 0.061 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.210 | 0.002 | 0.000 | 0.210 | 1 | 100 | 1.000 | 1.000 | 0.087 | 0.001 | 2.409 | 0.038 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.130 | 0.000 | 6.166 | 0.000 | -1 | 5 | NaN | NaN | 0.477 | 0.000 | 0.272 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 33.381 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.832 | 0.934 | 1.772 | 0.010 | 18.838 | 0.102 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.188 | 0.015 | 0.000 | 0.188 | -1 | 5 | 1.000 | 1.000 | 0.087 | 0.000 | 2.147 | 0.176 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.128 | 0.000 | 6.227 | 0.000 | -1 | 1 | NaN | NaN | 0.477 | 0.000 | 0.270 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.942 | 0.395 | 0.000 | 0.026 | -1 | 1 | 0.726 | 0.808 | 1.719 | 0.005 | 15.087 | 0.233 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.174 | 0.016 | 0.000 | 0.174 | -1 | 1 | 1.000 | 1.000 | 0.090 | 0.006 | 1.944 | 0.226 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.294 | 0.000 | 1 | 1 | NaN | NaN | 0.099 | 0.000 | 0.549 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.492 | 0.009 | 0.000 | 0.010 | 1 | 1 | 0.975 | 0.972 | 0.255 | 0.002 | 41.180 | 0.281 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.013 | 0.001 | 0.000 | 0.013 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 2.575 | 0.319 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.295 | 0.000 | -1 | 100 | NaN | NaN | 0.099 | 0.000 | 0.546 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.675 | 0.000 | 0.000 | 0.032 | -1 | 100 | 0.984 | 0.975 | 0.302 | 0.001 | 104.890 | 0.510 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.027 | 0.002 | 0.000 | 0.027 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 5.290 | 0.705 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.297 | 0.000 | 1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.543 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.087 | 0.044 | 0.000 | 0.019 | 1 | 5 | 0.980 | 0.963 | 0.254 | 0.001 | 75.286 | 0.259 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.001 | 0.000 | 0.021 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 4.214 | 0.379 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.295 | 0.000 | 1 | 100 | NaN | NaN | 0.099 | 0.000 | 0.551 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.145 | 0.015 | 0.000 | 0.019 | 1 | 100 | 0.984 | 0.963 | 0.254 | 0.002 | 75.265 | 0.593 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.022 | 0.001 | 0.000 | 0.022 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.365 | 0.398 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.297 | 0.000 | -1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.546 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.823 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.980 | 0.975 | 0.301 | 0.001 | 105.750 | 0.197 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.003 | 0.000 | 0.028 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.488 | 0.762 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.298 | 0.000 | -1 | 1 | NaN | NaN | 0.099 | 0.000 | 0.544 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.216 | 0.062 | 0.000 | 0.023 | -1 | 1 | 0.975 | 0.972 | 0.255 | 0.001 | 91.080 | 0.512 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.018 | 0.002 | 0.000 | 0.018 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.628 | 0.506 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.721 | 0.000 | 0.029 | 0.000 | 1 | 1 | NaN | NaN | 0.692 | 0.000 | 3.935 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.747 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.966 | 0.956 | 0.108 | 0.001 | 6.912 | 0.084 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.446 | 3.178 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.720 | 0.000 | 0.029 | 0.000 | -1 | 5 | NaN | NaN | 0.721 | 0.000 | 3.773 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.824 | 0.003 | 0.000 | 0.001 | -1 | 5 | 0.981 | 0.976 | 0.200 | 0.001 | 4.128 | 0.029 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 9.667 | 5.562 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.676 | 0.000 | 0.030 | 0.000 | -1 | 100 | NaN | NaN | 0.690 | 0.000 | 3.878 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.731 | 0.007 | 0.000 | 0.003 | -1 | 100 | 0.984 | 0.977 | 0.592 | 0.005 | 4.611 | 0.042 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 7.237 | 3.695 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.651 | 0.000 | 0.030 | 0.000 | -1 | 1 | NaN | NaN | 0.691 | 0.000 | 3.836 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.433 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.966 | 0.976 | 0.200 | 0.002 | 2.168 | 0.024 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 8.870 | 4.666 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.691 | 0.000 | 0.030 | 0.000 | 1 | 100 | NaN | NaN | 0.684 | 0.000 | 3.937 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.810 | 0.018 | 0.000 | 0.005 | 1 | 100 | 0.984 | 0.956 | 0.108 | 0.001 | 44.551 | 0.446 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 13.840 | 8.196 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.684 | 0.000 | 0.030 | 0.000 | 1 | 5 | NaN | NaN | 0.690 | 0.000 | 3.889 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.426 | 0.004 | 0.000 | 0.001 | 1 | 5 | 0.981 | 0.977 | 0.590 | 0.003 | 2.418 | 0.014 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 2.053 | 1.134 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.714 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.418 | 0.000 | 1.707 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.022 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.978 | 0.978 | 0.001 | 0.000 | 30.772 | 9.378 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.572 | 4.020 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.712 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.453 | 0.000 | 1.573 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.988 | 0.987 | 0.001 | 0.000 | 24.325 | 7.137 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 21.047 | 14.690 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.713 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.417 | 0.000 | 1.709 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.040 | 0.002 | 0.000 | 0.000 | -1 | 100 | 0.989 | 0.984 | 0.006 | 0.001 | 6.676 | 0.843 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 17.005 | 13.112 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.708 | 0.000 | 0.023 | 0.000 | -1 | 1 | NaN | NaN | 0.419 | 0.000 | 1.690 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.978 | 0.987 | 0.001 | 0.000 | 22.049 | 5.330 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.243 | 12.995 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.702 | 0.000 | 0.023 | 0.000 | 1 | 100 | NaN | NaN | 0.419 | 0.000 | 1.677 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.043 | 0.004 | 0.000 | 0.000 | 1 | 100 | 0.989 | 0.978 | 0.001 | 0.000 | 59.250 | 20.012 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 6.304 | 4.586 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.705 | 0.000 | 0.023 | 0.000 | 1 | 5 | NaN | NaN | 0.419 | 0.000 | 1.682 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.988 | 0.984 | 0.006 | 0.001 | 3.886 | 0.437 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.291 | 4.168 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.606 | 0.0 | 0.791 | 0.000 | k-means++ | NaN | 30 | NaN | 0.422 | 0.0 | 1.438 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.378 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.296 | 5.287 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.806 | 7.537 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.488 | 0.0 | 0.984 | 0.000 | random | NaN | 30 | NaN | 0.348 | 0.0 | 1.402 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.377 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.583 | 5.685 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.639 | 5.614 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.323 | 0.0 | 3.796 | 0.000 | k-means++ | NaN | 30 | NaN | 2.959 | 0.0 | 2.137 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.774 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.843 | 2.884 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.431 | 7.052 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.813 | 0.0 | 4.129 | 0.000 | random | NaN | 30 | NaN | 2.791 | 0.0 | 2.083 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.871 | 0.000 | random | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 5.776 | 2.967 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.931 | 6.917 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.212 | 0.0 | 0.015 | 0.000 | k-means++ | NaN | 20 | NaN | 0.084 | 0.0 | 2.517 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.193 | 0.000 | k-means++ | 0.001 | 20 | 0.000 | 0.001 | 0.0 | 2.714 | 0.411 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.366 | 5.458 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.072 | 0.0 | 0.044 | 0.000 | random | NaN | 20 | NaN | 0.029 | 0.0 | 2.488 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.194 | 0.000 | random | -0.001 | 20 | 0.002 | 0.001 | 0.0 | 2.704 | 0.535 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.777 | 5.392 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.549 | 0.0 | 0.291 | 0.000 | k-means++ | NaN | 20 | NaN | 0.305 | 0.0 | 1.803 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.809 | 0.000 | k-means++ | 0.326 | 20 | 0.279 | 0.001 | 0.0 | 2.223 | 0.357 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.187 | 3.814 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.188 | 0.0 | 0.851 | 0.000 | random | NaN | 20 | NaN | 0.123 | 0.0 | 1.532 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.816 | 0.000 | random | 0.283 | 20 | 0.258 | 0.001 | 0.0 | 2.217 | 0.378 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.032 | 3.941 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 10.893 | 0.0 | [-0.10831565] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.920 | 0.0 | 5.673 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [57.45015391] | 0.000 | NaN | NaN | NaN | NaN | 0.534 | 0.000 | 0.0 | 0.845 | 0.429 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.25216507] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.384 | 0.388 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.793 | 0.0 | [2.621351] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.761 | 0.0 | 1.043 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [133.75408424] | 0.000 | NaN | NaN | NaN | NaN | 0.300 | 0.003 | 0.0 | 0.548 | 0.113 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [25.14184595] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.122 | 0.094 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.176 | 0.0 | 0.455 | 0.0 | NaN | NaN | NaN | 0.177 | 0.000 | 0.991 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.011 | 0.0 | 7.152 | 0.0 | NaN | NaN | 0.109 | 0.018 | 0.001 | 0.625 | 0.030 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.092 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.677 | 0.611 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.356 | 0.0 | 0.590 | 0.0 | NaN | NaN | NaN | 0.227 | 0.000 | 5.965 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 4.444 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.810 | 0.920 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.646 | 0.673 | See | See |